edu::tum::cs::bayesnets::inference::JointBackwardSampling::BackSamplingDistribution Class Reference

Public Member Functions
void	addValue (double p, int[] state)
void	applyWeight (WeightedSample s, int sampledValue)
	BackSamplingDistribution (Sampler sampler)
void	construct (BeliefNode node, int[] nodeDomainIndices)
Public Attributes
Vector< Double >	distribution
Vector< int[]>	states
Protected Member Functions
void	construct (int i, int[] addr, CPF cpf, int[] nodeDomainIndices)
Protected Attributes
Sampler	sampler
Package Attributes
double	Z

Detailed Description

Definition at line 28 of file JointBackwardSampling.java.

edu::tum::cs::bayesnets::inference::JointBackwardSampling::BackSamplingDistribution::BackSamplingDistribution ( Sampler sampler ) [inline]

Definition at line 34 of file JointBackwardSampling.java.

void edu::tum::cs::bayesnets::inference::JointBackwardSampling::BackSamplingDistribution::addValue	(	double	p,
		int[]	state
	)			`[inline]`

Definition at line 41 of file JointBackwardSampling.java.

void edu::tum::cs::bayesnets::inference::JointBackwardSampling::BackSamplingDistribution::applyWeight	(	WeightedSample	s,
		int	sampledValue
	)			`[inline]`

Definition at line 47 of file JointBackwardSampling.java.

void edu::tum::cs::bayesnets::inference::JointBackwardSampling::BackSamplingDistribution::construct	(	int	i,
		int[]	addr,
		CPF	cpf,
		int[]	nodeDomainIndices
	)			`[inline, protected]`

recursively constructs the distribution to backward sample from

Parameters:

	i	the node to instantiate next (as an index into the CPF's domain product)
	addr	the current setting of node indices of the CPF's domain product
	cpf	the conditional probability function of the node we are backward sampling
	d	the distribution to fill

Definition at line 66 of file JointBackwardSampling.java.

void edu::tum::cs::bayesnets::inference::JointBackwardSampling::BackSamplingDistribution::construct	(	BeliefNode	node,
		int[]	nodeDomainIndices
	)			`[inline]`